Automatic Object Detection of Loess Landslide Based on Deep Learning

JU Yuanzhen; XU Qiang; JIN Shichao; LI Weile; DONG Xiujun; GUO Qinghua

doi:10.13203/j.whugis20200132

Volume 45 Issue 11

Nov. 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(11): 1747-1755. > DOI: 10.13203/j.whugis20200132

JU Yuanzhen, XU Qiang, JIN Shichao, LI Weile, DONG Xiujun, GUO Qinghua. Automatic Object Detection of Loess Landslide Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132

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Automatic Object Detection of Loess Landslide Based on Deep Learning

JU Yuanzhen^1,,
XU Qiang^1, ,,
JIN Shichao^{2, 3, 4},
LI Weile¹,
DONG Xiujun¹,
GUO Qinghua^{3, 4}

1.
State Key Laboratory of Geohazrd Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
2.
Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing 210095, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

Funds:

The Science Fund for Creative Research Groups of the National Natural Science Foundation of China 41521002

the Key Research and Development Program of the Department of Science and Technology of Sichuan Province 2019YFS0074

Sichuan Science and Technology Program 2018SZ0339

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Author Bio:
JU Yuanzhen, PhD candidate, specializes in geological disaster automatic detection. E-mail: 415384857@qq.com
Corresponding author:
XU Qiang, PhD, professor. E-mail: xq@cdut.edu.cn
Received Date: March 29, 2020
Published Date: November 18, 2020

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Abstract

Abstract

Objectives The knowledge of regional landslides detection plays a fundamental role in the landslide risk management. However, most of that recognition was taken manually in the past, which is rather time- and labor- consuming. As the development of technologies of remote sensing and artificial intelligence, the automatic detection of landslides becomes possible. The previous researches relative to the automatic detection of landslides utilized the machine learning methods to detect these new landslides which were significantly distinguished from their context. Compared to those landslides, the detection of old loess landslides that are not distinct from their context is more challenged. We explore the deep learning to automatically detect the old loess landslides.
Methods Firstly, we build a loess landslide database consists of 2 498 which are interpreted from the Google Earth images by experts. Then, we divide the database into three datasets for training, validation and test. Finally, we train Mask R-CNN object detection module with the training dataset, choose the best model by the validation dataset, and apply the best model to the test dataset.
Results The test results of model performance show a precision of 0.56, a recall of 0.72, and a F₁-score of 0.63.
Conclusions The results indicate that Mask R-CNN is a robust method even for the detection of loess landslides that are unapparent from the context, and deep learning can provide the possibility for rapid and accurate regional geo-hazard investigation.
- loess landslide,
- automatic detection,
- Google Earth image,
- deep learning,
- Mask R-CNN,
- object detection

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